RF-Source Seeking with Obstacle Avoidance using Real-time Modified Artificial Potential Fields in Unknown Environments

TL;DR

This paper introduces a real-time adaptive navigation method for UAVs that combines RF source seeking with obstacle avoidance using modified artificial potential fields, enabling effective operation in unknown environments.

Contribution

It presents a novel RF source seeking algorithm and a modified APF for dynamic obstacle avoidance, enhancing UAV navigation in unknown, obstacle-rich environments.

Findings

RF source seeking achieves 1.48° average angular error

Navigation success rate improves by 46%

Trajectory length reduces by 1.2%

Abstract

Navigation of UAVs in unknown environments with obstacles is essential for applications in disaster response and infrastructure monitoring. However, existing obstacle avoidance algorithms, such as Artificial Potential Field (APF) are unable to generalize across environments with different obstacle configurations. Furthermore, the precise location of the final target may not be available in applications such as search and rescue, in which case approaches such as RF source seeking can be used to align towards the target location. This paper proposes a real-time trajectory planning method, which involves real-time adaptation of APF through a sampling-based approach. The proposed approach utilizes only the bearing angle of the target without its precise location, and adjusts the potential field parameters according to the environment with new obstacle configurations in real time. The main contributions of the article are i) an RF source seeking algorithm to provide a bearing angle estimate using RF signal calculations based on antenna placement, and ii) a modified APF for adaptable collision avoidance in changing environments, which are evaluated separately in the simulation software Gazebo, using ROS2 for communication. Simulation results show that the RF source-seeking algorithm achieves high accuracy, with an average angular error of just 1.48 degrees, and with this estimate, the proposed navigation algorithm improves the success rate of reaching the target by 46% and reduces the trajectory length by 1.2% compared to standard potential fields.